code: def _get_x_prev_and_pred_x0(
      self,
      use_original_steps,
      b,
      index,
      device,
      x,
      t,
      model_output,
      e_t,
      quantize_denoised,
      repeat_noise,
      temperature,
      noise_dropout,
  ):
      alphas = (
          self.model.alphas_cumprod
          if use_original_steps
          else self.ddim_alphas
      )
      alphas_prev = (
          self.model.alphas_cumprod_prev
          if use_original_steps
          else self.ddim_alphas_prev
      )
      sqrt_one_minus_alphas = (
          self.model.sqrt_one_minus_alphas_cumprod
          if use_original_steps
          else self.ddim_sqrt_one_minus_alphas
      )
      sigmas = (
          self.model.ddim_sigmas_for_original_num_steps
          if use_original_steps
          else self.ddim_sigmas
      )
  
      # select parameters corresponding to the currently considered timestep
      a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
      a_prev = torch.full(
          (b, 1, 1, 1), alphas_prev[index], device=device
      )
      sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
      sqrt_one_minus_at = torch.full(
          (b, 1, 1, 1), sqrt_one_minus_alphas[index], device=device
      )
      # current prediction for x_0
      if self.model.parameterization != "v":
          pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
      else:
          pred_x0 = self.model.predict_start_from_z_and_v(
              x, t, model_output
          )
      if quantize_denoised:
          pred_x0, _, *_ = self.model.first_stage_model.quantize(
              pred_x0
          )
      # direction pointing to x_t
      dir_xt = (1.0 - a_prev - sigma_t**2).sqrt() * e_t
      noise = (
          sigma_t
          * noise_like(x.shape, device, repeat_noise)
          * temperature
      )
      if noise_dropout > 0.0:
          noise = torch.nn.functional.dropout(noise, p=noise_dropout)
      x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
      return x_prev, pred_x0
  
file_path: NeMo/nemo/collections/multimodal/models/text_to_image/stable_diffusion/samplers/base_sampler.py
imports: from abc import ABC, abstractmethod
  import numpy
  import torch
  from tqdm import tqdm
  from nemo.collections.multimodal.models.text_to_image.stable_diffusion.samplers import Sampler
  from nemo.collections.multimodal.modules.stable_diffusion.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like
label_desc: Use image node_id 10 for calling the AbstractBaseSampler obj's underlying member method code with example usage: obj._get_x_prev_and_pred_x0(use_original_steps, b, index, device, x, t, model_output, e_t, quantize_denoised, repeat_noise, temperature, noise_dropout) and returns: x_prev,  pred_x0